Self-supervision advances morphological profiling by unlocking powerful image representations

Abstract

Abstract Cell Painting is an image-based assay that offers valuable insights into drug mechanisms of action and off-target effects. However, traditional feature extraction tools such as CellProfiler are computationally intensive and require frequent parameter adjustments. Inspired by recent advances in AI, we trained self-supervised learning (SSL) models DINO, MAE, and SimCLR on subsets of the JUMP-CP dataset to obtain powerful image representations for Cell Painting. We assessed the reproducibility and biological relevance of SSL features and uncovered the critical factors influencing model performance, such as training set composition and domain-specific normalization techniques. Our best model (DINO) surpassed CellProfiler in drug target and gene family classification, significantly reducing computational time and costs. All SSL models showed remarkable generalizability without fine-tuning, outperforming CellProfiler on an unseen dataset of genetic perturbations. Our study demonstrates the effectiveness of SSL methods for morphological profiling, suggesting promising research directions for improving the analysis of related image modalities.